Performance of the Machine Learning on Controlling the Pneumatic Suspension of Automobiles on the Rigid and Off-Road Surfaces

Abstract

To enhance the ride comfort and control performance of the semi-active pneumatic suspension system (PSS) of automobiles on the different road surfaces, a machine learning method (MLM) developed on the optimal control rules of the fuzzy logic control is proposed for the semi-active PSS. A nonlinear dynamic model of the automobile with eight degrees of freedom (DOF) is established to compute the results. The root mean square (RMS) accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile are selected to evaluate the ride comfort of the automobile on the rigid road and off-road terrain surfaces. The research results show that the off-road terrain surfaces remarkably affect the ride comfort of the automobile, especially at a high moving speed range of the automobile over 17.5 m/s. The performance of the MLM in improving the ride comfort of the automobile is better than the fuzzy logic control under various simulation conditions. Particularly, the RMS accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile with the MLM are smaller than that of the fuzzy logic control by 14.6%, 9.6%, and 5.3% on the rigid road surfaces and reduced by 14.9%, 8.7%, and 9.8% on the soil terrain of off-road terrain surfaces, respectively. However, the research results also indicate that the performance of the MLM significantly depends on the data map of the learning process. Thus, to further enhance the performance of the MLM, the data map for the machine learning process should be expanded under different operating conditions of the automobile.

Assessing the Efficiency of the Air Isolation System Based on the Mathematical Models

“…where the air pressure p in the airbag chamber had been calculated by combination of Eqs. ( 14), ( 16)- (17), and (20).…”

“…This result is like the result in the existing studies. [37][38][39] When the AIS using the LPM and MFR is applied on the two-axle automobile, the results in Fig. 9 show that both the a wb and a wϕb ; and the DLC with the MFR are smaller than that of the LPM under the different speeds.…”

“…The moving speed of the vehicles greatly affected the ride comfort and road friendliness. 29,[37][38][39][40] Thus, to evaluate the isolation efficiency of the AIS using the LPM and MFR on improving road-friendliness and comfort in two-axle automobile, from operation parameters of the two-axle automobile listed in Table 2, 25,39 a vehicle's speed range from 2.5 to 40 m s −1 is simulated at a vibration excitation q 1 in Fig. 8.…”

“…This is due to the influence of the AIS with the parallel pipes or long pipes equipped on the two-axle automobiles. 2,16,39,40 Therefore, the MFR method should be used for designing and evaluating isolation efficiency of AIS for two-axle automobiles. This is the reason that the MFR method is always applied to evaluate the ride comfort of two-axle automobiles including ambulances and cars.…”

“…(1) Method of the lumped parameter model (LPM): From AIS's actual design parameters and thermodynamic theory, the dynamic parameters of the elastic stiffness, damping coefficient, static stiffness, and mass of the air in the pipe were calculated to establish the AIS's dynamic model. 1,4,[18][19][20] The studies showed that this method could independently evaluate the influence of the elastic stiffness, static stiffness, and damping coefficients of the AIS using complex pipes. Concurrently, these parameters were easily optimized to enhance the isolation efficiency of the AIS.…”